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import logging
import torch
from os import path as osp
from basicsr.data import build_dataloader, build_dataset
from basicsr.models import build_model
from basicsr.utils import get_env_info, get_root_logger, get_time_str, make_exp_dirs
from basicsr.utils.options import dict2str, parse_options
def test_pipeline(root_path):
# parse options, set distributed setting, set ramdom seed
opt, _ = parse_options(root_path, is_train=False)
torch.backends.cudnn.benchmark = True
# torch.backends.cudnn.deterministic = True
# mkdir and initialize loggers
make_exp_dirs(opt)
log_file = osp.join(opt['path']['log'], f"test_{opt['name']}_{get_time_str()}.log")
logger = get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=log_file)
logger.info(get_env_info())
logger.info(dict2str(opt))
# create test dataset and dataloader
test_loaders = []
for _, dataset_opt in sorted(opt['datasets'].items()):
test_set = build_dataset(dataset_opt)
test_loader = build_dataloader(
test_set, dataset_opt, num_gpu=opt['num_gpu'], dist=opt['dist'], sampler=None, seed=opt['manual_seed'])
logger.info(f"Number of test images in {dataset_opt['name']}: {len(test_set)}")
test_loaders.append(test_loader)
# create model
model = build_model(opt)
for test_loader in test_loaders:
test_set_name = test_loader.dataset.opt['name']
logger.info(f'Testing {test_set_name}...')
model.validation(test_loader, current_iter=opt['name'], tb_logger=None, save_img=opt['val']['save_img'])
if __name__ == '__main__':
root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
test_pipeline(root_path)
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